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Published in 2018 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2018.05.005
Abstract: Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different…
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Keywords:
segmentation;
brain tumour;
texture abnormality;
tumour segmentation ... See more keywords
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Published in 2020 at "Automatika"
DOI: 10.1080/00051144.2020.1760590
Abstract: Brain tumour segmentation evolved as the dominant task in brain image processing. Most of the contemporary research proposals devise deep neural networks and sparse representation to address this issue. These methods inherently suffer from high…
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Keywords:
optimized net;
brain tumour;
model;
tumour segmentation ... See more keywords
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Published in 2020 at "Insights into Imaging"
DOI: 10.1186/s13244-020-00869-4
Abstract: The introduction of quantitative image analysis has given rise to fields such as radiomics which have been used to predict clinical sequelae. One growing area of interest for analysis is brain tumours, in particular glioblastoma…
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Keywords:
brain tumours;
neural networks;
segmentation;
convolutional neural ... See more keywords
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Published in 2023 at "Diagnostics"
DOI: 10.3390/diagnostics13030363
Abstract: In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation…
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Keywords:
brain tumour;
tumour segmentation;
deep learning;
learning ... See more keywords